We introduce the concept of a compendium as both a container for the different elements that make up the document and its computations (i.e. text, code, data, …), and as a means for distributing, managing and updating the collection.

We introduce the concept of a compendium as both a container for the different elements that make up the document and its computations (i.e. text, code, data, …), and as a means for distributing, managing and updating the collection.

• README.md
• a synopsis of the project
• very useful for your future self and colleges/collaborators

• NEWS.md
• Communicate change to the project files/data

• DESCRIPTION (R specific - Python requirements.txt)
• LICENSE
• .gitIgnore (Will talk about Git later)

• Project meta data
• Legal documents (e.g. contracts)
• Communications
• Ethics documentation
• Indirect resources (e.g. papers)
• Project management resources
• etc.

* Consider adding directories/files marked with an asterisk to .gitIgnore

• Old code that might be redundant, but is precious
• Helps to keep project folders tidy

• A “read-only” directory to store raw data (e.g. Excel, STATA, SAS, .csv)
• Recommend changing the file to read only in operating system
• The heart of reproducible research, all actions are traceable from raw data to report

• Data is converted from the raw format in to the software’s preferred structure (e.g. .RData, .rds)
• Minimal operations are performed on data
  • correcting variable names
  • data cleaning
  • data harmonisation/standardisation/coding
  • table joining/splitting

• Store manipulated data here for statistical analysis

library('ProjectTemplate')
load.project()

• Automates data initiation
• Loads library
• Runs munge scripts
• Loads data

• http://projecttemplate.net/getting_started.html

Verb (used with or without object)

To manipulate (raw data), especially to convert (data) from one format to another:

• A ProjectTemplate directory
• Order numerically in the order the scripts should be run:
  • 01-munge.R
  • 02-Lexis.R

• 00-cleaner.R - read in raw data and save in data
• 01-main.R - main script file
• 99-helper.R - custom functions to be read in at start of script
• data.R - How you can create a data dictionary in R!

• config
• ProjectTemplate configuration files, e.g.:
  • Auto-munge
  • Auto-load packages
  • Auto-import cache/data
• inst
• Auto generated files used by some package-related functions
• Vignettes

Markdown is a lightweight mark-up language with plain text formatting syntax that allows it to be converted to many output formats.

R Markdown documents are fully reproducible. Use a productive notebook interface to weave together narrative text and code to produce elegantly formatted output. Use multiple languages including R, Python, and SQL.

Live demo

• Be consistent:
  • Values:
    • “day1” vs “day_1” vs “Day 1”
    • “5th May 1970” vs “01-05-1970” vs “05/01/1970” vs 1970/05/01
  • Variables:
    • FirstName vs first_name
    • sex vs female
• Use variable names that a human can understand
• Document/comment your code!

• R Studio IDE
• Tidyverse - data wrangling and visualisation
• repmis: Miscellaneous Tools for Reproducible Research
• captioner: Store figure and table captions and print them later
• devtools
• Telethon Kids Biometrics package https://github.com/TelethonKids/biometrics